Auditory model representation and comparison for speaker recognition

The TIMIT and KING databases are used to compare proven spectral processing techinques to an auditory neural representation for speaker identification. The feature sets compared are linear prediction coding (LPC) cepstral coefficients and auditory nerve firing rates using the Payton model (1988). Two clustering algorithms, one statistically based and the other a neural approach, are used to generate speaker-specific codebook vectors. These algorithms are the Linde-Buzo-Gray algorithm and a Kohonen self-organizing feature map. The resulting vector-quantized distortion-based classification indicates the auditory model performs statistically equal to the LPC cepstral representation in clean environments and outperforms the LPC cepstral in noisy environments and in test data recorded over multiple sessions (greater intra-speaker distortions)